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Three-dimensional active contour model for characterization of solid breast masses on three-dimensional ultrasound images

机译:用于在三维超声图像上表征固体乳房肿块的三维主动轮廓模型

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The accuracy of discrimination between malignant and benign solid breast masses on ultrasound images may be improved by using computer-aided diagnosis and 3-D information. The purpose of this study was to develop automated 3-D segmentation and classification methods for 3-D ultrasound images, and to compare the classification accuracy based on 2-D and 3-D segmentation techniques. The 3-D volumes were recorded by translating the transducer across the lesion in the z-direction while conventional 2-D images were acquired in the x-y plane. 2-D and 3-D segmentation methods based on active contour models were developed to delineate the mass boundaries. Features were automatically extracted based on the segmented mass shapes, and were merged into a malignancy score using a linear classifier. 3-D volumes containing biopsy-proven solid breast masses were collected from 102 patients (44 benign and 58 malignant). A leave-one-out method was used for feature selection and classifier design. The area A_z under the test receiver operating characteristic curves for the classifiers using the 3-D and 2-D active contour boundaries were 0.88 and 0.84, respectively. More than 45% of the benign masses could be correctly identified using the 3-D features without missing a malignancy. Our results indicate that an accurate computer classifier can be designed for differentiation of malignant and benign solid breast masses on 3-D sonograms.
机译:通过使用计算机辅助诊断和3D信息,可以提高超声图像上恶性和良性乳腺肿块的鉴别准确性。这项研究的目的是为3-D超声图像开发自动化的3-D分割和分类方法,并比较基于2-D和3-D分割技术的分类精度。通过在z方向上横穿病灶平移换能器来记录3-D体积,同时在x-y平面中获取常规2-D图像。开发了基于主动轮廓模型的2-D和3-D分割方法来描绘质量边界。根据分段的质量形状自动提取特征,并使用线性分类器将其合并为恶性评分。从102例患者(44例良性和58例恶性)中收集了包含活检证实的实性乳腺肿块的3-D容积。留一法用于特征选择和分类器设计。使用3-D和2-D有效轮廓边界的分类器在测试接收机工作特性曲线下的面积A_z分别为0.88和0.84。使用3-D功能可以正确识别超过45%的良性肿块,而不会遗漏恶性肿瘤。我们的结果表明,可以设计一种准确的计算机分类器,以区分3D超声图上的恶性和良性固体乳腺肿块。

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